KEGG: new perspectives on genomes, pathways, diseases and drugs

Abstract

KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.

Biallelic Mutations in CFAP43 and CFAP44 Cause Male Infertility with Multiple Morphological Abnormalities of the Sperm Flagella

“…Then we used ANNOVAR for functional annotation with OMIM, Gene Ontology, KEGG Pathway, SIFT, PolyPhen-2, MutationTaster, and the Exome Aggregation Consortium (ExAC) Browser. [23][24][25][26][27][28][29] The candidate pathogenic mutations and their parental origins were verified by Sanger sequencing.…”

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

“…Moreover, similar enrichment analysis against pathway databases KEGG,35 Reactome,36 and Gene Ontology (GO)37, 38 reveals that these 31 genes are also enriched for “pancreatic cancer” (KEGG pathway, P value = 9.1 × 10 −5 , FDR = 2.1 × 10 −3 , Figure 5c), “R‐HSA‐912526” (Reactome pathway, P value = 7.3 × 10 −5 , FDR = 1.1 × 10 −2 , Figure 5d), and “protein phosphorylation” (GO biological process, P value = 1.3 × 10 −4 , FDR = 9.3 × 10 −3 , Figure 5e). Specifically, PIK3CB (Figure 5a,c,d) ranked 286th and 291th by MaxMIF with HumanNet and STRINGv10, was hypothesized as a potential oncogene in certain cancers,39 and has been subsequently demonstrated as an oncogene,40 although it has not yet been added to the CGC list under this version.…”

“…Thus, MaxMIF could be used to identify unknown cancer driver genes. Indeed, by considering the non‐CGC candidates ranked by MaxMIF above the 500th with both the HumanNet and STRINGv10 datasets, we identified some potential novel driver mutation genes with strong independent evidence supports in GAD,31 KEGG pathway,35 Reactome pathway,36 and GO biological process 37, 38…”

Baseline urine metabolic phenotype in patients with severe alcoholic hepatitis and its association with outcome

“…In brief, genes overexpressed in the liver compared to the PBMCs were found to be liver specific (henceforth, liver‐specific genes), while genes underexpressed in the liver compared to the PBMCs were considered mainly related to immune cell functions (henceforth, immune‐cell‐related genes). Because patients who had transcriptomic results were also enrolled in the present metabolomics study, we combined “omics” data sets using the following strategy: first, among the sets of liver‐specific genes and immune‐cell‐related genes, we identified genes that were differentially expressed between NRs and Rs; second, we used hierarchical clustering to identify gene clusters that accounted for differences between NRs and Rs, according to the method by Li et al26 We used Gene Set Enrichment Analysis (http://software.broadinstitute.org/gsea/index.jsp)27 to query the open source databases of Kyoto Encyclopedia of Genes and Genomes (http://www.genome.ad.jp/kegg/),28 REACTOME (https://reactome.org/), and Gene Ontology (http://www.geneontology.org), with the aim to functionally characterize gene clusters. Gene sets or pathways were considered as relevant when they included at least five genes and P < 0.05 and the false discovery rate was <0.05.…”